@proceedings {221,
	title = {A Federated MapReduce-based Video Transcoding System to Face the Future Massive Video-Selfie Sharing Trend},
	journal = {Fourth European Conference on Service-Oriented and Cloud Computing (ESOCC 2015) -  3rd International Workshop on Cloud for IoT},
	year = {2015},
	publisher = {Springer International Publishing},
	edition = {Communications in Computer and Information Science (CCSI)},
	abstract = {<pre>
The massive use of mobile devices and social networks is causing the birth of a new compulsive users{\textquoteright} behaviour. The activity photo <u>selfie</u> sharing is gradually turning into video <u>selfie</u>. These videos will be transcoded into multiple formats to support different visualization mode. We think there will be the need to have systems that can support, in a fast, efficient and scalable way, the millions of requests for video sharing and viewing. We think that a single Cloud Computing services provider cannot alone cope with this huge amount of incoming data (Big Data), so in this paper we propose a Cloud Federation-based system that exploiting the <u>Hadoop</u> <u>MapReduce</u> paradigm performs the video transcoding in multiple format and its distribution in a fastest and most efficient possible way. Experimental results highlight the major factors involved for job deployment in a federated Cloud environment and the efficiency of the proposed system.</pre>
},
	keywords = {Adaptive Streaming, Apache Hadoop, Big Data, CLEVER, cloud computing, HDFS, Horizontal Federation, IEEE P2302, MapReduce},
	author = {Alfonso Panarello and Antonio Celesti and Maria Fazio and Massimo Villari and Antonio Puliafito}
}
